Follow
Rafael Cabañas
Title
Cited by
Cited by
Year
AMIDST: A Java toolbox for scalable probabilistic machine learning
AR Masegosa, AM Martínez, D Ramos-López, R Cabañas, A Salmerón, ...
Knowledge-Based Systems 163, 595-597, 2019
212019
Evaluating interval-valued influence diagrams
R Cabañas, A Antonucci, A Cano, M Gómez-Olmedo
International Journal of Approximate Reasoning 80, 393-411, 2017
122017
InferPy: Probabilistic modeling with TensorFlow made easy
R Cabañas, A Salmerón, AR Masegosa
Knowledge-Based Systems 168, 25-27, 2019
102019
Approximate inference in influence diagrams using binary trees
RC de Paz, M Gómez-Olmedo, A Cano
Proceedings of the 6th European Workshop on Probabilistic Graphical Models …, 2012
102012
Financial data analysis with PGMs using AMIDST
R Cabañas, AM Martínez, AR Masegosa, D Ramos-López, A Samerón, ...
2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW …, 2016
82016
Structural causal models are (solvable by) credal networks
M Zaffalon, A Antonucci, R Cabañas
International Conference on Probabilistic Graphical Models, 581-592, 2020
72020
CREMA: a Java library for credal network inference
D Huber, R Cabañas, A Antonucci, M Zaffalon
International Conference on Probabilistic Graphical Models, 613-616, 2020
62020
Virtual subconcept drift detection in discrete data using probabilistic graphical models
R Cabañas, A Cano, M Gómez-Olmedo, AR Masegosa, S Moral
International Conference on Information Processing and Management of …, 2018
52018
Improvements to variable elimination and symbolic probabilistic inference for evaluating influence diagrams
R Cabañas, A Cano, M Gómez-Olmedo, AL Madsen
International Journal of Approximate Reasoning 70, 13-35, 2016
52016
On SPI for evaluating influence diagrams
R Cabanas, AL Madsen, A Cano, M Gómez-Olmedo
International Conference on Information Processing and Management of …, 2014
52014
Probabilistic models with deep neural networks
AR Masegosa, R Cabañas, H Langseth, TD Nielsen, A Salmerón
Entropy 23 (1), 117, 2021
42021
InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy
J Cózar, R Cabañas, A Salmerón, AR Masegosa
Neurocomputing 415, 408, 2020
42020
Using binary trees for the evaluation of influence diagrams
R Cabanas, M Gomez-Olmedo, A Cano
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems …, 2016
42016
On SPI-lazy evaluation of influence diagrams
R Cabañas, A Cano, M Gómez-Olmedo, AL Madsen
European Workshop on Probabilistic Graphical Models, 97-112, 2014
42014
Variable Elimination for Interval-Valued Influence Diagrams
R Cabanas, A Antonucci, A Cano, M Gómez-Olmedo
32015
Value-based potentials: exploiting quantitative information regularity patterns in probabilistic graphical models
M Gómez-Olmedo, R Cabañas, A Cano, S Moral, OP Retamero
International Journal of Intelligent Systems, 2021
22021
Causal expectation-maximisation
M Zaffalon, A Antonucci, R Cabañas
arXiv preprint arXiv:2011.02912, 2020
22020
Long-term survival in diffuse infiltrative brainstem gliomas in children and adolescents: treated with radiotherapy and Nimotuzumab
J Alert, I Chon, R Cabanas, J Reno, D Garcia, M Perez, R Ropero
Int J Radiol Radiat Ther 5 (4), 267-270, 2018
22018
Heuristics for determining the elimination ordering in the influence diagram evaluation with binary trees
RCA CANO, M GOMEZ-OLMEDO, AL MADSEN
Twelfth Scandinavian Conference on Artificial Intelligence: SCAI 2013 257, 65, 2013
22013
Approximate lazy evaluation of influence diagrams
R Cabanas, A Cano, M Gómez-Olmedo, AL Madsen
Conference of the Spanish Association for Artificial Intelligence, 321-331, 2013
22013
The system can't perform the operation now. Try again later.
Articles 1–20